Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations7024
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory823.1 KiB
Average record size in memory120.0 B

Variable types

DateTime1
Categorical1
Text5
Numeric7

Alerts

NO2(GT) is highly overall correlated with NOx(GT) and 4 other fieldsHigh correlation
NOx(GT) is highly overall correlated with NO2(GT) and 4 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with NO2(GT) and 5 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with NO2(GT) and 5 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with NO2(GT) and 5 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with PT08.S1(CO) and 3 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with NO2(GT) and 5 other fieldsHigh correlation

Reproduction

Analysis started2025-11-28 09:28:55.110431
Analysis finished2025-11-28 09:29:04.853348
Duration9.74 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Date
Date

Distinct341
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-28T10:29:04.950616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:05.139384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Categorical

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
10.00.00
 
313
06.00.00
 
312
20.00.00
 
311
00.00.00
 
311
12.00.00
 
311
Other values (19)
5466 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters56192
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
10.00.00313
 
4.5%
06.00.00312
 
4.4%
20.00.00311
 
4.4%
00.00.00311
 
4.4%
12.00.00311
 
4.4%
07.00.00311
 
4.4%
21.00.00310
 
4.4%
18.00.00310
 
4.4%
11.00.00310
 
4.4%
09.00.00310
 
4.4%
Other values (14)3915
55.7%

Length

2025-11-28T10:29:05.297898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.00.00313
 
4.5%
06.00.00312
 
4.4%
20.00.00311
 
4.4%
00.00.00311
 
4.4%
12.00.00311
 
4.4%
07.00.00311
 
4.4%
21.00.00310
 
4.4%
18.00.00310
 
4.4%
11.00.00310
 
4.4%
09.00.00310
 
4.4%
Other values (14)3915
55.7%

Most occurring characters

ValueCountFrequency (%)
031724
56.5%
.14048
25.0%
14019
 
7.2%
22163
 
3.8%
3644
 
1.1%
6621
 
1.1%
7619
 
1.1%
9619
 
1.1%
5616
 
1.1%
8615
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)56192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
031724
56.5%
.14048
25.0%
14019
 
7.2%
22163
 
3.8%
3644
 
1.1%
6621
 
1.1%
7619
 
1.1%
9619
 
1.1%
5616
 
1.1%
8615
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
031724
56.5%
.14048
25.0%
14019
 
7.2%
22163
 
3.8%
3644
 
1.1%
6621
 
1.1%
7619
 
1.1%
9619
 
1.1%
5616
 
1.1%
8615
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
031724
56.5%
.14048
25.0%
14019
 
7.2%
22163
 
3.8%
3644
 
1.1%
6621
 
1.1%
7619
 
1.1%
9619
 
1.1%
5616
 
1.1%
8615
 
1.1%

CO(GT)
Text

Distinct101
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:05.595435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.8957859
Min length1

Characters and Unicode

Total characters20340
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.2%

Sample

1st row2,6
2nd row2
3rd row2,2
4th row2,2
5th row1,6
ValueCountFrequency (%)
1,4259
 
3.7%
1,6252
 
3.6%
1,5251
 
3.6%
1,1234
 
3.3%
1,3230
 
3.3%
1,7228
 
3.2%
1,2228
 
3.2%
0,7223
 
3.2%
0,9215
 
3.1%
1,9215
 
3.1%
Other values (91)4689
66.8%
2025-11-28T10:29:06.039962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,6531
32.1%
13025
14.9%
22320
 
11.4%
01769
 
8.7%
31544
 
7.6%
41196
 
5.9%
5953
 
4.7%
6854
 
4.2%
7733
 
3.6%
9669
 
3.3%
Other values (2)746
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)20340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,6531
32.1%
13025
14.9%
22320
 
11.4%
01769
 
8.7%
31544
 
7.6%
41196
 
5.9%
5953
 
4.7%
6854
 
4.2%
7733
 
3.6%
9669
 
3.3%
Other values (2)746
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,6531
32.1%
13025
14.9%
22320
 
11.4%
01769
 
8.7%
31544
 
7.6%
41196
 
5.9%
5953
 
4.7%
6854
 
4.2%
7733
 
3.6%
9669
 
3.3%
Other values (2)746
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,6531
32.1%
13025
14.9%
22320
 
11.4%
01769
 
8.7%
31544
 
7.6%
41196
 
5.9%
5953
 
4.7%
6854
 
4.2%
7733
 
3.6%
9669
 
3.3%
Other values (2)746
 
3.7%

PT08.S1(CO)
Real number (ℝ)

High correlation 

Distinct1017
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1117.6644
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:06.194470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile820
Q1955
median1083
Q31252
95-th percentile1521
Maximum2040
Range1393
Interquartile range (IQR)297

Descriptive statistics

Standard deviation218.81667
Coefficient of variation (CV)0.19578029
Kurtosis0.26781623
Mean1117.6644
Median Absolute Deviation (MAD)145
Skewness0.70625414
Sum7850475
Variance47880.733
MonotonicityNot monotonic
2025-11-28T10:29:06.374574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97325
 
0.4%
110023
 
0.3%
93822
 
0.3%
98822
 
0.3%
96921
 
0.3%
101620
 
0.3%
106520
 
0.3%
97020
 
0.3%
100920
 
0.3%
102119
 
0.3%
Other values (1007)6812
97.0%
ValueCountFrequency (%)
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
< 0.1%
6691
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6832
< 0.1%
6892
< 0.1%
6911
 
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%
Distinct393
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:06.908592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4325171
Min length3

Characters and Unicode

Total characters24110
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)0.9%

Sample

1st row11,9
2nd row9,4
3rd row9,0
4th row9,2
5th row6,5
ValueCountFrequency (%)
3,665
 
0.9%
2,861
 
0.9%
4,057
 
0.8%
5,357
 
0.8%
3,857
 
0.8%
3,156
 
0.8%
6,055
 
0.8%
5,254
 
0.8%
2,554
 
0.8%
5,453
 
0.8%
Other values (383)6455
91.9%
2025-11-28T10:29:07.547509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7024
29.1%
13636
15.1%
22114
 
8.8%
31684
 
7.0%
41510
 
6.3%
51492
 
6.2%
61452
 
6.0%
71357
 
5.6%
01337
 
5.5%
81272
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)24110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7024
29.1%
13636
15.1%
22114
 
8.8%
31684
 
7.0%
41510
 
6.3%
51492
 
6.2%
61452
 
6.0%
71357
 
5.6%
01337
 
5.5%
81272
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7024
29.1%
13636
15.1%
22114
 
8.8%
31684
 
7.0%
41510
 
6.3%
51492
 
6.2%
61452
 
6.0%
71357
 
5.6%
01337
 
5.5%
81272
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7024
29.1%
13636
15.1%
22114
 
8.8%
31684
 
7.0%
41510
 
6.3%
51492
 
6.2%
61452
 
6.0%
71357
 
5.6%
01337
 
5.5%
81272
 
5.3%

PT08.S2(NMHC)
Real number (ℝ)

High correlation 

Distinct1198
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean954.51139
Minimum383
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:07.707889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum383
5-th percentile570.15
Q1754
median928
Q31132
95-th percentile1425
Maximum2214
Range1831
Interquartile range (IQR)378

Descriptive statistics

Standard deviation265.60228
Coefficient of variation (CV)0.27825994
Kurtosis0.027017967
Mean954.51139
Median Absolute Deviation (MAD)189.5
Skewness0.5012005
Sum6704488
Variance70544.571
MonotonicityNot monotonic
2025-11-28T10:29:07.883544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88021
 
0.3%
77619
 
0.3%
89618
 
0.3%
80018
 
0.3%
92417
 
0.2%
80317
 
0.2%
98517
 
0.2%
94517
 
0.2%
93116
 
0.2%
82816
 
0.2%
Other values (1188)6848
97.5%
ValueCountFrequency (%)
3832
< 0.1%
3881
< 0.1%
3902
< 0.1%
3971
< 0.1%
3991
< 0.1%
4022
< 0.1%
4072
< 0.1%
4081
< 0.1%
4091
< 0.1%
4122
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

NOx(GT)
Real number (ℝ)

High correlation 

Distinct896
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.23078
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:08.055836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile40
Q1102
median185
Q3332
95-th percentile684
Maximum1479
Range1477
Interquartile range (IQR)230

Descriptive statistics

Standard deviation208.01717
Coefficient of variation (CV)0.83463674
Kurtosis3.2023861
Mean249.23078
Median Absolute Deviation (MAD)101
Skewness1.6536801
Sum1750597
Variance43271.142
MonotonicityNot monotonic
2025-11-28T10:29:08.234188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8936
 
0.5%
18034
 
0.5%
13233
 
0.5%
6533
 
0.5%
4132
 
0.5%
5132
 
0.5%
9332
 
0.5%
9532
 
0.5%
12232
 
0.5%
16631
 
0.4%
Other values (886)6697
95.3%
ValueCountFrequency (%)
21
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
102
< 0.1%
114
0.1%
123
< 0.1%
134
0.1%
143
< 0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13011
< 0.1%
12901
< 0.1%
12471
< 0.1%
12301
< 0.1%
12201
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

High correlation 

Distinct1144
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean819.89052
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:08.403505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile476
Q1645
median788
Q3949
95-th percentile1270
Maximum2683
Range2361
Interquartile range (IQR)304

Descriptive statistics

Standard deviation253.92098
Coefficient of variation (CV)0.30970108
Kurtosis3.0935583
Mean819.89052
Median Absolute Deviation (MAD)151
Skewness1.1777878
Sum5758911
Variance64475.862
MonotonicityNot monotonic
2025-11-28T10:29:08.953332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73320
 
0.3%
70219
 
0.3%
73719
 
0.3%
70519
 
0.3%
80019
 
0.3%
76519
 
0.3%
84619
 
0.3%
75118
 
0.3%
56718
 
0.3%
68118
 
0.3%
Other values (1134)6836
97.3%
ValueCountFrequency (%)
3221
< 0.1%
3252
< 0.1%
3281
< 0.1%
3302
< 0.1%
3341
< 0.1%
3351
< 0.1%
3401
< 0.1%
3411
< 0.1%
3451
< 0.1%
3461
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20771
< 0.1%
20621
< 0.1%

NO2(GT)
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.57133
Minimum2
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:09.119790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile44
Q179
median110
Q3142
95-th percentile200
Maximum333
Range331
Interquartile range (IQR)63

Descriptive statistics

Standard deviation47.541354
Coefficient of variation (CV)0.41860349
Kurtosis0.33334668
Mean113.57133
Median Absolute Deviation (MAD)32
Skewness0.58045591
Sum797725
Variance2260.1803
MonotonicityNot monotonic
2025-11-28T10:29:09.287638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11973
 
1.0%
11772
 
1.0%
11072
 
1.0%
11471
 
1.0%
9570
 
1.0%
10168
 
1.0%
11668
 
1.0%
11567
 
1.0%
9767
 
1.0%
10767
 
1.0%
Other values (264)6329
90.1%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
145
0.1%
ValueCountFrequency (%)
3331
 
< 0.1%
3221
 
< 0.1%
3101
 
< 0.1%
3091
 
< 0.1%
3061
 
< 0.1%
3011
 
< 0.1%
2951
 
< 0.1%
2882
< 0.1%
2851
 
< 0.1%
2833
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

High correlation 

Distinct1545
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1446.5834
Minimum551
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:09.457171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile868
Q11194.75
median1452
Q31681
95-th percentile2032
Maximum2775
Range2224
Interquartile range (IQR)486.25

Descriptive statistics

Standard deviation355.89469
Coefficient of variation (CV)0.24602431
Kurtosis-0.10160819
Mean1446.5834
Median Absolute Deviation (MAD)241
Skewness0.20271797
Sum10160802
Variance126661.03
MonotonicityNot monotonic
2025-11-28T10:29:09.624532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148818
 
0.3%
153916
 
0.2%
163815
 
0.2%
141815
 
0.2%
149015
 
0.2%
158015
 
0.2%
155214
 
0.2%
154714
 
0.2%
143514
 
0.2%
125714
 
0.2%
Other values (1535)6874
97.9%
ValueCountFrequency (%)
5511
< 0.1%
5611
< 0.1%
5791
< 0.1%
6011
< 0.1%
6021
< 0.1%
6051
< 0.1%
6211
< 0.1%
6371
< 0.1%
6401
< 0.1%
6421
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26431
< 0.1%
26412
< 0.1%
26221
< 0.1%
26091
< 0.1%

PT08.S5(O3)
Real number (ℝ)

High correlation 

Distinct1702
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1053.3304
Minimum221
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:09.787897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile465
Q1754
median1003
Q31318
95-th percentile1798
Maximum2523
Range2302
Interquartile range (IQR)564

Descriptive statistics

Standard deviation407.56279
Coefficient of variation (CV)0.38692776
Kurtosis-0.031437051
Mean1053.3304
Median Absolute Deviation (MAD)278
Skewness0.54741963
Sum7398593
Variance166107.43
MonotonicityNot monotonic
2025-11-28T10:29:09.960962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83618
 
0.3%
90515
 
0.2%
92615
 
0.2%
82515
 
0.2%
94914
 
0.2%
85314
 
0.2%
105914
 
0.2%
82614
 
0.2%
80714
 
0.2%
100613
 
0.2%
Other values (1692)6878
97.9%
ValueCountFrequency (%)
2211
< 0.1%
2251
< 0.1%
2321
< 0.1%
2521
< 0.1%
2531
< 0.1%
2571
< 0.1%
2612
< 0.1%
2621
< 0.1%
2631
< 0.1%
2661
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24941
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%

T
Text

Distinct430
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:10.483758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7927107
Min length3

Characters and Unicode

Total characters26640
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.3%

Sample

1st row13,6
2nd row13,3
3rd row11,9
4th row11,0
5th row11,2
ValueCountFrequency (%)
12,044
 
0.6%
21,343
 
0.6%
20,243
 
0.6%
20,842
 
0.6%
13,542
 
0.6%
12,341
 
0.6%
13,741
 
0.6%
13,440
 
0.6%
13,340
 
0.6%
16,338
 
0.5%
Other values (412)6610
94.1%
2025-11-28T10:29:11.128481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7024
26.4%
14285
16.1%
23315
12.4%
32283
 
8.6%
41521
 
5.7%
51434
 
5.4%
61419
 
5.3%
01374
 
5.2%
81353
 
5.1%
71333
 
5.0%
Other values (2)1299
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)26640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7024
26.4%
14285
16.1%
23315
12.4%
32283
 
8.6%
41521
 
5.7%
51434
 
5.4%
61419
 
5.3%
01374
 
5.2%
81353
 
5.1%
71333
 
5.0%
Other values (2)1299
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7024
26.4%
14285
16.1%
23315
12.4%
32283
 
8.6%
41521
 
5.7%
51434
 
5.4%
61419
 
5.3%
01374
 
5.2%
81353
 
5.1%
71333
 
5.0%
Other values (2)1299
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7024
26.4%
14285
16.1%
23315
12.4%
32283
 
8.6%
41521
 
5.7%
51434
 
5.4%
61419
 
5.3%
01374
 
5.2%
81353
 
5.1%
71333
 
5.0%
Other values (2)1299
 
4.9%

RH
Text

Distinct742
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:11.631676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9991458
Min length3

Characters and Unicode

Total characters28090
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.4%

Sample

1st row48,9
2nd row47,7
3rd row54,0
4th row60,0
5th row59,6
ValueCountFrequency (%)
47,825
 
0.4%
45,923
 
0.3%
53,123
 
0.3%
43,422
 
0.3%
50,821
 
0.3%
50,121
 
0.3%
50,921
 
0.3%
39,421
 
0.3%
42,821
 
0.3%
57,921
 
0.3%
Other values (732)6805
96.9%
2025-11-28T10:29:12.264819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7024
25.0%
42756
 
9.8%
52717
 
9.7%
32650
 
9.4%
62493
 
8.9%
22084
 
7.4%
72042
 
7.3%
81720
 
6.1%
11681
 
6.0%
91485
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)28090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7024
25.0%
42756
 
9.8%
52717
 
9.7%
32650
 
9.4%
62493
 
8.9%
22084
 
7.4%
72042
 
7.3%
81720
 
6.1%
11681
 
6.0%
91485
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7024
25.0%
42756
 
9.8%
52717
 
9.7%
32650
 
9.4%
62493
 
8.9%
22084
 
7.4%
72042
 
7.3%
81720
 
6.1%
11681
 
6.0%
91485
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7024
25.0%
42756
 
9.8%
52717
 
9.7%
32650
 
9.4%
62493
 
8.9%
22084
 
7.4%
72042
 
7.3%
81720
 
6.1%
11681
 
6.0%
91485
 
5.3%

AH
Text

Distinct5503
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2025-11-28T10:29:12.685959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters42144
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4258 ?
Unique (%)60.6%

Sample

1st row0,7578
2nd row0,7255
3rd row0,7502
4th row0,7867
5th row0,7888
ValueCountFrequency (%)
0,74876
 
0.1%
0,87365
 
0.1%
1,11995
 
0.1%
0,66865
 
0.1%
0,97225
 
0.1%
0,94674
 
0.1%
0,88784
 
0.1%
0,91464
 
0.1%
0,83254
 
0.1%
0,47874
 
0.1%
Other values (5493)6978
99.3%
2025-11-28T10:29:13.233160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7024
16.7%
06670
15.8%
15694
13.5%
42967
7.0%
92961
7.0%
72901
6.9%
82892
6.9%
62810
6.7%
32784
 
6.6%
52727
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)42144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7024
16.7%
06670
15.8%
15694
13.5%
42967
7.0%
92961
7.0%
72901
6.9%
82892
6.9%
62810
6.7%
32784
 
6.6%
52727
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7024
16.7%
06670
15.8%
15694
13.5%
42967
7.0%
92961
7.0%
72901
6.9%
82892
6.9%
62810
6.7%
32784
 
6.6%
52727
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7024
16.7%
06670
15.8%
15694
13.5%
42967
7.0%
92961
7.0%
72901
6.9%
82892
6.9%
62810
6.7%
32784
 
6.6%
52727
 
6.5%

Interactions

2025-11-28T10:29:03.587984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:57.373354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.337822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.301126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:00.249528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:01.769323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.701879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.722460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:57.516465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.479965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.442831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:00.383674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:01.904790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.835087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.855034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:57.666087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.621235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.584216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:00.518119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.048008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.967648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.988842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:57.806951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.764645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.720505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:00.648627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.185861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.100723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:04.115998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:57.936749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.895506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.849518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:01.394330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.313589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.219418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:04.246024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.073845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.033653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.988628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:01.529720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.444615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.347502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:04.364212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:58.207155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:28:59.167667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:00.118865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:01.650501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:02.573515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T10:29:03.467083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-28T10:29:13.353750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
NO2(GT)NOx(GT)PT08.S1(CO)PT08.S2(NMHC)PT08.S3(NOx)PT08.S4(NO2)PT08.S5(O3)Time
NO2(GT)1.0000.8280.6600.657-0.6790.1430.7050.133
NOx(GT)0.8281.0000.7180.704-0.7950.1650.7940.071
PT08.S1(CO)0.6600.7181.0000.883-0.8480.6430.8970.097
PT08.S2(NMHC)0.6570.7040.8831.000-0.8350.7510.8740.132
PT08.S3(NOx)-0.679-0.795-0.848-0.8351.000-0.511-0.8630.089
PT08.S4(NO2)0.1430.1650.6430.751-0.5111.0000.5510.075
PT08.S5(O3)0.7050.7940.8970.874-0.8630.5511.0000.082
Time0.1330.0710.0970.1320.0890.0750.0821.000

Missing values

2025-11-28T10:29:04.567105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-28T10:29:04.746081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateTimeCO(GT)PT08.S1(CO)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
010/03/200418.00.002,61360.011,91046.0166.01056.0113.01692.01268.013,648,90,7578
110/03/200419.00.0021292.09,4955.0103.01174.092.01559.0972.013,347,70,7255
210/03/200420.00.002,21402.09,0939.0131.01140.0114.01555.01074.011,954,00,7502
310/03/200421.00.002,21376.09,2948.0172.01092.0122.01584.01203.011,060,00,7867
410/03/200422.00.001,61272.06,5836.0131.01205.0116.01490.01110.011,259,60,7888
510/03/200423.00.001,21197.04,7750.089.01337.096.01393.0949.011,259,20,7848
611/03/200400.00.001,21185.03,6690.062.01462.077.01333.0733.011,356,80,7603
711/03/200401.00.0011136.03,3672.062.01453.076.01333.0730.010,760,00,7702
811/03/200402.00.000,91094.02,3609.045.01579.060.01276.0620.010,759,70,7648
1111/03/200405.00.000,71066.01,1512.016.01918.028.01182.0422.011,056,20,7366
DateTimeCO(GT)PT08.S1(CO)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
934704/04/200505.00.000,5888.01,3528.077.01077.053.0987.0578.010,459,90,7550
934804/04/200506.00.001,11031.04,4730.0182.0760.093.01129.0905.09,563,10,7531
934904/04/200507.00.004,01384.017,41221.0594.0470.0155.01600.01457.09,761,90,7446
935004/04/200508.00.005,01446.022,41362.0586.0415.0174.01777.01705.013,548,90,7553
935104/04/200509.00.003,91297.013,61102.0523.0507.0187.01375.01583.018,236,30,7487
935204/04/200510.00.003,11314.013,51101.0472.0539.0190.01374.01729.021,929,30,7568
935304/04/200511.00.002,41163.011,41027.0353.0604.0179.01264.01269.024,323,70,7119
935404/04/200512.00.002,41142.012,41063.0293.0603.0175.01241.01092.026,918,30,6406
935504/04/200513.00.002,11003.09,5961.0235.0702.0156.01041.0770.028,313,50,5139
935604/04/200514.00.002,21071.011,91047.0265.0654.0168.01129.0816.028,513,10,5028